THE RELATION BETWEEN SELF-EFFICACY, INJURY AND FEAR OF INJURY AMONG ELITE ATHLETES
Bibliographic record
Abstract
Background A growing body of research recognizes the significance of the psychological response to injury among athletes. Yet, to date there is paucity of research that conceptualizes the role which self-efficacy plays in an athlete's response to injury (Wiese-Bjornstal, 2010; Feltz et al., 2008). Objective To analysis the hypothetical relation between the types of injuries and the level of self-efficacy and fear to avoid future injuries. Design Quantitative retrospective survey style for correlational and regression analysis. Setting Elite male athletes in the Canadian Football League (CFL) at preseason training. Participants 365 pre-season survey reports from CFL players, convenience sampling. Risk factor assessment Measures included a self-report measure of self-efficacy (measuring fears around injuries, perceptual control of injuries, and self-efficacy to avoid injuries) and a structured report of the frequency, severity and location of injuries sustained by the athlete in a playing season. Main outcome measurements Two main hypotheses were set out before the analysis. First was that injuries would relate to an athlete's level of self-efficacy to avoid injuries in a negative direction. Second was that fears of injuries and perceptual control of injuries could predict an athlete's level of self-efficacy. Results Our results demonstrated significant correlations between self-efficacy to injury frequency, severity, and fears of re-injury. Regression analysis revealed variables (injuries, fear of injuries and perceptual control) significantly predict self-efficacy. Results also demonstrate that the location of injury also significantly impact levels of self-efficacy. Conclusions This study builds on the research of understanding how athletes are affected by injuries psychologically and how they can deal with the fears and anxieties due to injuries. Practically these results, with the amounting research, can help medical and psychological treatments in dealing with an athlete's self-efficacy and fears.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".